


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

A			:						str        =  [
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]



class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		bool ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		list = None						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						None
							lowercase__	     =						os.path.abspath(os.path.join("""examples""" ,   """by_feature"""						)						)
							lowercase__	     =						os.path.abspath("""examples"""						)
							for item in os.listdir(_UpperCAmelCase						):
									if item not in EXCLUDE_EXAMPLES:
											lowercase__	     =						os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						)
											if os.path.isfile(_UpperCAmelCase						) and ".py" in item_path:
													with self.subTest(
													    tested_script=_UpperCAmelCase ,   feature_script=_UpperCAmelCase ,   tested_section="""main()""" if parser_only else """training_function()""" ,   ):
															lowercase__	     =						compare_against_test(
															    os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						) ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
															lowercase__	     =						"""\n""".join(_UpperCAmelCase						)
															if special_strings is not None:
																	for string in special_strings:
																			lowercase__	     =						diff.replace(_UpperCAmelCase ,   """"""						)
															self.assertEqual(_UpperCAmelCase ,   """"""						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  int:




							"""simple docstring"""


							self.one_complete_example("""complete_nlp_example.py""" ,   _UpperCAmelCase						)
							self.one_complete_example("""complete_nlp_example.py""" ,   _UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						os.path.abspath(os.path.join("""examples""" ,   """cv_example.py"""						)						)
							lowercase__	     =						[
							    """ """ * 16 + """{\n\n""",
							    """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
							    """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
							    """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
							    """ """ * 20 + """\"epoch\": epoch,\n\n""",
							    """ """ * 16 + """},\n\n""",
							    """ """ * 16 + """step=epoch,\n""",
							    """ """ * 12,
							    """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
							]
							self.one_complete_example("""complete_cv_example.py""" ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							self.one_complete_example("""complete_cv_example.py""" ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)






@mock.patch.dict(os.environ					,    {'''TESTING_MOCKED_DATALOADERS''': '''1'''}				)
class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					False

					@classmethod
					def     lowerCamelCase__	(cls       :		Tuple						)		->  int:




							"""simple docstring"""


							super().setUpClass()
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						os.path.join(cls._tmpdir ,   """default_config.yml"""						)

							write_basic_config(save_location=cls.configPath						)
							lowercase__	     =						["""accelerate""", """launch""", """--config_file""", cls.configPath]

					@classmethod
					def     lowerCamelCase__	(cls       :		List[Any]						)		->  int:




							"""simple docstring"""


							super().tearDownClass()
							shutil.rmtree(cls._tmpdir						)

					def     lowerCamelCase__	(self       :		str						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
							run_command(self._launch_args + testargs						)
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,   """epoch_0"""						)						)						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
							lowercase__	     =						run_command(self._launch_args + testargs						)
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,   """step_2"""						)						)						)

					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir ,   "epoch_0"						)}
        '''.split()
							lowercase__	     =						run_command(self._launch_args + testargs ,   return_stdout=_UpperCAmelCase						)
							self.assertNotIn("""epoch 0:""" ,   _UpperCAmelCase						)
							self.assertIn("""epoch 1:""" ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir ,   "step_2"						)}
        '''.split()
							lowercase__	     =						run_command(self._launch_args + testargs ,   return_stdout=_UpperCAmelCase						)
							if torch.cuda.is_available():
									lowercase__	     =						torch.cuda.device_count()
							else:
									lowercase__	     =						1
							if num_processes > 1:
									self.assertNotIn("""epoch 0:""" ,   _UpperCAmelCase						)
									self.assertIn("""epoch 1:""" ,   _UpperCAmelCase						)
							else:
									self.assertIn("""epoch 0:""" ,   _UpperCAmelCase						)
									self.assertIn("""epoch 1:""" ,   _UpperCAmelCase						)

					@slow
					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
							with mock.patch.dict(os.environ ,   {"""TESTING_MOCKED_DATALOADERS""": """0"""}						):
									lowercase__	     =						run_command(self._launch_args + testargs ,   return_stdout=_UpperCAmelCase						)
									lowercase__	     =						re.findall("""({.+})""" ,   _UpperCAmelCase						)
									lowercase__	     =						[r for r in results if """accuracy""" in r][-1]
									lowercase__	     =						ast.literal_eval(_UpperCAmelCase						)
									self.assertGreaterEqual(results["""accuracy"""] ,   0.75						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						["""examples/by_feature/multi_process_metrics.py"""]
							run_command(self._launch_args + testargs						)

					@require_trackers
					@mock.patch.dict(os.environ ,   {"""WANDB_MODE""": """offline"""}						)
					def     lowerCamelCase__	(self       :		List[str]						)		->  str:




							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
									lowercase__	     =						f'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
									run_command(self._launch_args + testargs						)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase ,   """tracking"""						)						)						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						["""examples/by_feature/gradient_accumulation.py"""]
							run_command(self._launch_args + testargs						)


					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						["""examples/by_feature/local_sgd.py"""]
							run_command(self._launch_args + testargs						)





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=7     ,   __snake_case=3     ,   __snake_case=3_0     ,   __snake_case=4_0_0     ,   __snake_case=True     ,   __snake_case=None     ,   __snake_case=True     ,   __snake_case=1 / 2_5_5     ,   __snake_case=True     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=True     ,   ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         snake_case											=							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							num_channels
         snake_case											=							min_resolution
         snake_case											=							max_resolution
         snake_case											=							do_resize
         snake_case											=							size
         snake_case											=							do_rescale
         snake_case											=							rescale_factor
         snake_case											=							do_normalize
         snake_case											=							image_mean
         snake_case											=							image_std
         snake_case											=							do_pad
    def 						a_     (			self       ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         if not batched:
              snake_case											=							image_inputs[0]
              if isinstance(__snake_case     ,   Image.Image       ):
                   snake_case					,    snake_case											=							image.size
              else:
                   snake_case					,    snake_case											=							image.shape[1], image.shape[2]
              if w < h:
                   snake_case											=							int(self.size['''shortest_edge'''] * h / w       )
                   snake_case											=							self.size['''shortest_edge''']
              elif w > h:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							int(self.size['''shortest_edge'''] * w / h       )
              else:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							self.size['''shortest_edge''']

         else:
              snake_case											=							[]
              for image in image_inputs:
                   snake_case					,    snake_case											=							self.get_expected_values([image]       )
                   expected_values.append((expected_height, expected_width)       )
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[0]       )[0]
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						DetrImageProcessor if is_vision_available() else None
    def 						a_     (			self       ):
         snake_case											=							DetrImageProcessingTester(self       )
    @property
    def 						a_     (			self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_mean'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_std'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_normalize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_rescale'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''rescale_factor'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_resize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''size'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_pad'''       )       )
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )

         snake_case											=							self.image_processing_class.from_dict(
             self.image_processor_dict     ,   size=4_2     ,   max_size=8_4     ,   pad_and_return_pixel_mask=__snake_case       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   Image.Image       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   numpify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   np.ndarray       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   torch.Tensor       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    @slow
    def 						a_     (			self       ):
         # prepare image and target
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




    @slow
    def 						a_     (			self       ):
         # prepare image, target and masks_path
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

         snake_case											=							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   masks_path=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify masks
         snake_case											=							8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,   __snake_case       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




